In this work a model-free arterial spin labeling (ASL) quantification approach for measuring cerebral blood flow (CBF) and arterial blood volume (aBV) is proposed. The method is based on the acquisition of a train of multiple images following the labeling scheme. Perfusion is obtained using deconvolution in a manner similar to that of dynamic susceptibility contrast (DSC) MRI. Local arterial input functions (AIFs) can be estimated by subtracting two perfusion-weighted images acquired with and without crusher gradients, respectively. Furthermore, by knowing the duration of the bolus of tagged arterial blood, one can estimate the aBV on a voxel-by-voxel basis. The maximum of the residue function obtained from the deconvolution of the tissue curve by the AIF is a measure of CBF after scaling by the locally estimated aBV. This method provides averaged gray matter (GM) perfusion values of 38 +/- 2 ml/min/100 g and aBV of 0.93% +/- 0.06%. The average CBF value is 10% smaller than that obtained on the same data set using the standard general kinetic model (42 +/- 2 ml/min/100 g). Monte Carlo simulations were performed to compare this new methodology with parametric fitting by the conventional model.